Emotional food craving: Highest during positive emotions, and calorie-specific during negative emotions.

Normal emotional experience depends on dynamic modulation of neural excitability across limbic and prefrontal circuits, yet the spectral markers that reflect these shifts in humans remain incompletely understood. In this study, we combined a validated video-based emotion induction paradigm with stereotactic electroencephalography (SEEG) in 31 patients with drug-resistant epilepsy to investigate how positive and negative affective states modulate oscillatory and aperiodic (asynchronous) neural activity. Using spectral parameterization to dissociate oscillatory power from the aperiodic 1/f component, we found that emotional valence robustly altered the aperiodic slope in a regionally specific manner: negative valence flattened the slope in thalamus, posterior insula, and posterior cingulate cortex, whereas positive valence produced flattening in dorsolateral prefrontal cortex. Simultaneous oscillatory changes included increased high-frequency activity and decreased alpha/beta power during negative affect, and reduced alpha power during positive affect, which were elucidated after adjusting for broadband aperiodic spectral shifts. These effects persisted after controlling for audiovisual stimulus or physiological features and were not evident in simultaneously recorded scalp EEG, underscoring their localization to intracranial sites. Together, these results provide the first direct evidence that active induction of emotional states modulates the aperiodic slope of human intracranial field potentials, reflecting valence-dependent shifts in local circuit excitability. The findings highlight the 1/f slope as a sensitive neural marker of affective brain states and for mood dysregulation.

ABSTRACT Narrative dance uses a repertoire of symbolic dance movements and mimetic and abstract expressive gestures accompanied by various musical elements for meaning generation. This study seeks to gain new insight into entrainment or sensorimotor synchronization between dance movements and musical rhythm for meaning generation in classical ballet solo dance through interviews with choreographers, ballet artistic directors, and senior teachers. The results reveal that entrainment can be seen as involving kinesemiotics (movement-based semiotic systems), metaphorical corporeal articulation (symbolic bodily expression), and expressive gestures for meaning generation and narrative embodiment. It also may arise from emotional induction and kinesthetic empathy (a process in which audiences experience dance through their own sense of movement), resulting from watching and listening to the synchronicity of dance movements and musical beats, where the audio-visual synchrony creates a multimodal rhythm. Each form and level of entrainment, in terms of its duration, sequence, the body parts involved, and the intensity of the beat synchronization, can have semiotic, metaphoric, and/or emotional impact, which may evoke kinesthetic empathy and lead to the emergence of specific meanings.

Emotional states are known to modulate tremor severity in Wilson's disease (WD), but the neural mechanisms underlying this emotion-tremor coupling remain poorly understood. This study aimed to investigate the neurophysiological and structural substrates of emotion-induced tremor variability in WD patients using electroencephalography (EEG) microstate analysis, kinematic tremor tracking, and structural MRI. Forty-five tremor-dominant WD patients and 20 healthy controls underwent assessments with the Body Image Disturbance Questionnaire (BIDQ), Fahn-Tolosa-Marin Tremor Rating Scale (FTM-TRS), Self-Assessment Manikin (SAM), and Facial Expression Recognition (FER) tasks. Tremor kinematics were quantified via Kinovea, and EEG microstates were analyzed during emotion induction. Structural magnetic resonance imaging (MRI) was used to evaluate brain atrophy patterns. WD patients exhibited greater social avoidance (Z = -5.721, p < 0.05), prolonged FER reaction times (1.78s vs. 1.05s, p < 0.001), and more negative face selections (6 vs. 5, p = 0.036). Negative emotional states were associated with significantly larger tremor amplitude (3.79 px vs. 3.16 px, p = 0.012). EEG microstates showed increased frequency of microstate C (4.74/min vs. 3.41/min, p < 0.001) and coverage (16.62% vs. 13.22%, p < 0.001) during negative emotion, which correlated with tremor severity (ρ = 0.319, p = 0.039). Regression analysis identified lentiform nucleus damage (β = 0.361), cerebellar atrophy (β = 0.300), and frontal atrophy (β = -0.386) as predictors of emotional arousal (R² = 0.277, p = 0.042). Emotion-tremor coupling in WD involves dysregulated salience networks and cerebellar-frontal-lentiform circuits, with EEG microstate C as a potential marker for targeted interventions.

Emotional egocentricity bias is modulated by implicit expectations of interpersonal emotional contingencies and perceptual noise.

Background: Substance Use Disorder (SUD) is characterized by recurrent craving episodes frequently associated with emotional dysregulation and altered reward processing. This study aimed to evaluate whether emotional states associated with craving episodes can be detected through automated facial emotion recognition during controlled emotional induction. Methods: Forty-one participants completed a 14-day ecological momentary assessment (EMA) monitoring anxiety and craving levels, followed by an emotional induction task using standardized stimuli from the EmoMadrid database and addiction-related images. Facial expressions were recorded and analyzed in real time using a computational facial emotion recognition model trained on the FER-2013 dataset. Results: Participants with SUD exhibited significantly reduced positive emotional valence and emotional activation in response to positive stimuli compared with healthy controls (HC), with large effect sizes observed for emotional valence (Hedges’ g = 1.76) and emotional activation (Hedges’ g = 1.33). Item-level analyses revealed that most between-group differences occurred in stimuli depicting social interactions. Individuals with SUD also showed higher frequencies of fear-related facial expressions and lower frequencies of disgust-related expressions compared with HC, with moderate effect sizes observed for both emotional dimensions (Hedges’ g = 0.72; p = 0.02). Conclusions: These results suggest that people with SUD have changes in how they process emotions, showing less response to positive things and unique facial expressions related to craving. However, given the relatively modest and clinically heterogeneous sample, the findings should be interpreted cautiously and require replication in larger and more homogeneous populations.

Emotion recognition from physiological signals typically treats emotions as discrete, static states rather than dynamic processes, limiting real-world affective computing applications. This dataset contains multimodal physiological recordings from 28 participants experiencing systematically designed emotional transitions in virtual reality. Participants viewed emotion-eliciting video stimuli across three emotional quadrants with transition periods between stimuli. Four physiological modalities were recorded: EEG (7 channels, 300 Hz), ECG (4 leads, 512 Hz), EMG (2 channels, 512 Hz), and GSR (3 channels, 10 Hz). The protocol employed a balanced incomplete block design across six emotional sequences. Statistical validation shows quadrant differentiation with 70% physiologically validated and 85% self-reported emotion induction success rates on average. Individual journey analysis indicates that participants traversed between 8.84% and 58.39% of the theoretical maximum cumulative distance on the valence-arousal plane, reflecting substantial individual differences in emotional responsivity. The dataset comprises 1.84 GB of original XDF recordings, 238 video-aligned physiological segments, and self-assessment ratings. This resource enables research in dynamic emotion recognition, and individual differences in responsivity during controlled emotional transitions.

Intensive longitudinal data are often found to be non-stationary, namely, showing changes in statistical properties, such as means and variance-covariance structures, over time. One way to accommodate non-stationarity is to specify key parameters that show over-time changes as time-varying parameters (TVPs). However, the nature and dynamics of TVPs may themselves be heterogeneous across time, contexts, developmental stages, individuals and as related to other biopsychosocial-cultural influences. We propose an outlier detection method designed to facilitate the detection of critical shifts in any differentiable linear and non-linear dynamic functions, including dynamic functions for TVPs. This approach can be readily applied to various data scenarios, including single-subject and multisubject, univariate and multivariate processes, as well as with and without latent variables. We demonstrate the utility and performance of this approach with three sets of simulation studies and an empirical illustration using facial electromyography data from a laboratory emotion induction study.

Depression affects more than 300 million people worldwide and is a leading contributor to the global disease burden. Traditional diagnostic methods, such as structured clinical interviews, are reliable but impractical for frequent or large-scale screening. Self-report tools like the Patient Health Questionnaire-8 (PHQ-8) require disclosure and clinician oversight, limiting accessibility. Recent artificial intelligence-based approaches leverage multimodal behavioral cues (linguistic, acoustic, and visual) for automated depression detection but remain constrained by limited adaptability, scarce annotated data, weak emotional expression in real-world settings, and the high computational cost of deployment of socially assistive robots (SARs). This study introduces Depression Social Assistant Robot (DEPRESAR)-Fusion, a lightweight multimodal depression detection framework designed for natural interactions with emotion-aware SARs. The objective of this study was to enhance detection accuracy in everyday conversations while addressing the challenges of data scarcity, weak emotional cues, and computational efficiency. DEPRESAR-Fusion integrates acoustic, linguistic, and visual features with an emotion-aware response module powered by large language models to adapt conversational strategies dynamically. To stimulate richer emotional expression, participants were exposed to emotionally evocative videos before SAR interactions. To overcome data scarcity, we augmented training with (1) public depression-related social media corpora and (2) synthetic samples generated via large language models. The proposed multimodal fusion architecture was evaluated on benchmark clinical datasets for both binary depression classification and PHQ-8 regression tasks. Performance was compared against prior multimodal baselines using root mean square error, mean absolute error, and standard classification metrics. Participants who viewed emotional stimuli before interacting with SARs exhibited significantly higher emotional expressiveness, leading to improved model performance. Regression tasks showed lower root mean square error and mean absolute error, while classification tasks achieved significantly higher accuracy than the nonstimulus condition. DEPRESAR-Fusion outperformed prior multimodal baselines across multiple benchmark datasets, achieving state-of-the-art performance in both binary classification and PHQ-8 regression. The system maintained a lightweight architecture suitable for real-time deployment on SARs. DEPRESAR-Fusion demonstrates that integrating emotion induction, data augmentation, and lightweight multimodal fusion can enable accurate and scalable depression detection in naturalistic SAR interactions. By bridging the gap between structured clinical assessments and everyday conversations, this approach highlights the potential of SAR-based systems as nonintrusive, artificial intelligence-driven tools for proactive mental health support.

Theoretical and empirical work suggests that guilt motivates reparative and relational actions; yet, its potential to promote prosocial deception in early childhood remains unclear. Across two studies, we examined whether guilt fosters prosocial lying in 4- to 5-year-olds. In Study 1 (N = 133, 67 girls, 66 boys; middle-income Chinese families), children induced to feel guilt were more likely to tell polite and helping lies to support a victim than those in a baseline, no emotion induction condition. Study 2 (N = 124, 62 girls, 62 boys) compared the effect of guilt induction on prosocial lying toward a victim versus a bystander. Whereas guilt increased polite and helping lies to benefit a victim (replicating Study 1), guilt only increased polite, but not helping lies, directed at a bystander. These findings suggest that guilt induction increased the likelihood of prosocial lying in preschoolers, but its motivational influence varied by social context. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Urban residents require space-efficient interventions to mitigate chronic stress. While indoor digital nature shows promise, the precise impact of interactive design parameters remains unclear. This study investigated how interactive feedback intensity (none, slow, fast) and color hue (neutral, warm, cool) influence psychological and physiological restoration. Following negative emotion induction, healthy participants engaged in within-subject conditions evaluated via multimodal assessments, including EEG, HRV, and subjective scales (PANAS, PRS, SAM/PAD). Results identified interactive feedback intensity as the primary driver of restoration. Specifically, fast feedback improved positive affect by up to 20.4% and reduced negative affect by 20.8% compared to passive self-restoration. Neurologically, interactive engagement was associated with elevated EEG alpha-band activity by up to 97.8% relative to standing controls, a pattern consistent with cortical relaxation. Furthermore, while physical interaction was uniformly associated with physiological indices broadly consistent with recovery, color hue significantly moderated subjective outcomes. Neutral and warm hues generated significantly higher overall perceived restorativeness (M = 73.18 and M = 70.14, respectively) than the self-restoration control (M = 61.26). Notably, neutral tones were uniquely associated with modest changes in HRV time-domain indices suggestive of parasympathetic autonomic modulation. These findings provide actionable, empirically validated guidelines for deploying responsive digital interventions to support mental well-being in dense urban interiors.

We propose an emotion recognition dataset based on millimeter-wave (mmWave) radar and physiological reference signals. Compared to conventional methods, mmWave radar could obtain vital signs in a non-contact method without privacy concerns. We used validated stimuli to induce participants’ emotions and simultaneously recorded three types of signals: mmWave signals, photoplethysmography (PPG) pulse signals, and galvanic skin response (GSR) signals. Participants used the Self-Assessment Manikin (SAM) to provide subjective emotion ratings. We collected signals and emotion rating data from 15 participants and validated the effectiveness of emotion induction and the data quality. The dataset can be used for research such as: (1) mmWave radar-based vital sign extraction; (2) comparison of emotion recognition performance across different signals; (3) multi-modal fusion for emotion recognition; (4) individual differences in emotional responses; (5) cross-subject emotion recognition, among others.

Service robots are increasingly used in retail and service contexts, yet how emotional cues influence consumer decisions when the salesperson is a robot remains underexplored. Using a between-subjects experiment that manipulated emotional induction and salesperson type (human vs. robot), this study examined the effect of emotional induction on purchase intention, the mediating role of perceived emotions, and the moderating role of salesperson type. Positive induction increased purchase intention, whereas negative induction decreased it. Perceived emotions significantly mediated the relationship between emotional induction and purchase intention. Salesperson type further shaped these effects: in positive contexts, the facilitating effect was stronger for human salespersons, whereas in negative contexts, the inhibiting effect was stronger for robot salespersons. In addition, the effect of perceived emotions on purchase intention varied by salesperson type. These findings reveal valence-dependent boundary effects in human–robot selling interactions and clarify the emotional process underlying consumer purchase decisions.

ObjectiveHuman emotions could affect pain perception, but knowledge from well-powered experiments about how different emotions affect pressure pain thresholds (PPTs) in pain-free individuals are missing. The aim of this quasi-randomized control trial was to investigate the effect of different emotional states on PPTs in four different body locations (upper, right m. trapezius; upper, left m. trapezius; right m. tibialis anterior; left m. tibialis anterior).MethodsPain-free participants (n = 152) were assigned to four different emotional states (negative n = 38; positive n = 38; distraction control n = 38; control n = 38). Baseline PPTs in each group were measured after a neutral video clip following emotional state induction with video clips (negative; positive; distraction control; control). PPTs were again measured after emotion induction.ResultsThe main finding was that negative emotion induction significantly lowered PPTs in the m. trapezius and m. tibialis anterior on the left side of the body after correction for multiple comparisons. PPTs were also significantly lower in the left m. tibialis anterior in the distraction control group. No other significant differences in PPT levels were found.ConclusionNegative emotions, but not positive emotions, could significantly lower PPTs in the left m. trapezius and the left m. tibialis anterior in healthy individuals. A practical implication may be that negative emotions might negatively affect pain states through descending pain modulation.

Facial expression recognition (FER) in naturalistic settings is constrained by label ambiguity and variability in stimulus-response alignment. Adopting a data-centric perspective, this study examined whether emotional intelligence (EI)-stratified training data influence FER performance by treating EI as a qualitative factor associated with affective data consistency. Naturally elicited facial expressions were collected in a controlled emotion induction experiment with subjective arousal and valence ratings. Using response-driven labeling, neutral ratings were retained as indicators of ambiguity. Participants were grouped into High and Low EI based on the alignment between subjective evaluations and outputs from a pretrained affect estimator. Identical binary classifiers for arousal and valence recognition were trained while varying only the training data composition and evaluated across baseline, unambiguous, and ambiguous test sets using independent training repetitions with repetition-level statistical aggregation. EI-stratified training was associated with statistically detectable, context-dependent performance differences: group effects were observed primarily under baseline conditions and, to a lesser extent, under ambiguous conditions, whereas no reliable differences emerged under unambiguous conditions. Pooled discrimination differences were modest, but item-level analyses identified significant differences in classification correctness in specific task-condition combinations. Comparable patterns were observed across alternative backbone architectures. These findings indicate that FER performance in naturalistic contexts is influenced not only by model architecture but also by the statistical structure and internal coherence of the training data, supporting EI-informed data selection in ambiguity-prone scenarios.

The effect of script-driven emotional imagery on postural control in healthy individuals.

Music can induce emotions and is often used to enhance emotional experiences of storytelling media, while sound effects can convey information on a story’s environmental setting. While these non-speech sounds are well-integrated into traditional media, their use in newer forms such as robotic storytelling is still developing. To address this gap, we developed guidelines for emotion-inducing music based on theoretical knowledge from music theory, psychology, and media studies, and validated them in an online perception study. Subsequently, a laboratory prestudy compared the effects of the music’s source during robotic storytelling, finding no significant differences between the robotic storyteller and an external loudspeaker. Building on these results, our main study compared storytelling with added background music, sound effects, a combination of both, and a control condition without non-speech sounds. Results showed that while subjective evaluations of presentation liking and qualitative feedback did not significantly differ, background music alone yielded the best outcomes on standardized measures, enhancing transportation, cognitive absorption, emotion induction, and objectively assessed attention-related affects. These findings support incorporating emotion-inducing background music into robotic storytelling to enhance its immersive and emotional effects. • Theory-based and empirically tested guidelines for emotion-inducing music. • Source of accompanying non-speech sounds does not influence storytelling experience nor robot perception. • Background music outperforms sound effects as well as combination or omission of both in terms of storytelling experience and induced affects.

Controlling for emotional valence and arousal, this study examined how the motivational direction (approach/withdrawal) and intensity of emotions influence inhibitory and switching functions of attentional control in individuals with depressive symptoms. Experiment 1 used emotion induction and a dual-choice Oddball paradigm to assess the impact of emotional motivation on inhibitory function. Results showed that individuals with depressive symptoms exhibited weaker approach motivation toward food and stronger avoidance motivation toward sadness in the inhibition task. Under approach motivation, high-intensity emotion impaired inhibition, while low-intensity emotion facilitated it. Under withdrawal motivation, both high- and low-intensity emotions impaired inhibition. Experiment 2 used emotion induction and a task-switching paradigm to assess the impact on switching function. Results showed that individuals with depressive symptoms displayed more comprehensive motivational deficits in the switching task. Beyond abnormal motivations toward food and sadness, funny scenes failed to induce approach emotions. Approach emotions had no effect on switching function, whereas both high- and low-avoidance emotions hindered it. The findings indicate that individuals with depressive symptoms exhibit emotional motivational deficits. The influence of emotional motivation on inhibitory and switching functions is inconsistent: inhibitory function may be jointly modulated by the direction and intensity of emotional motivation, while switching function is primarily influenced by the direction of motivation.

Abstract Background With the rapid development of e-commerce and social media, online marketing has been deeply integrated into the daily consumption decision-making process of individuals. Compared to general consumers, individuals with mental disorders exhibit certain functional limitations in emotional regulation, impulse control, and risk assessment, making them more susceptible to the high-frequency information stimulation and emotional induction prevalent in online marketing environments. Existing research primarily focuses on the psychological mechanisms of online marketing effects on the general population, while studies addressing the consumption behavior characteristics and influencing factors of this special group remain relatively limited. Based on this, the present study aims to explore the consumption behavior characteristics and influencing factors of individuals with mental disorders in online marketing environments, providing a scientific basis for improving online consumption protection mechanisms and related mental health interventions for special populations. Methods This study employed a cross-sectional survey design, selecting 180 patients with mental disorders from a follow-up management program at a mental health medical institution in a certain city. The participants ranged in age from 20 to 55 years, all possessed basic internet usage skills, and were in a stable phase of their condition. Structured questionnaires were used to collect demographic information, internet usage habits, and consumption behavior characteristics. The primary measurement tools included the Online Marketing Susceptibility Scale (OMSS), the Impulsiveness Scale (Barratt Impulsiveness Scale, BIS-11), and a consumption behavior questionnaire. The relationship between online marketing factors and consumption behavior was analyzed using independent samples t-test, Pearson correlation analysis, and multiple linear regression analysis. The statistical significance level was set at p&amp;lt;.05. Results In the context of online marketing, the consumption behavior of patients with mental disorders is closely related to their sensitivity to online marketing and impulsive behavior levels. Correlation analysis indicates that Online Marketing Sensitivity Score (OMSS) shows a significant positive correlation with average monthly unplanned consumption expenditure (r = 0.43, p&amp;lt;.001) and a moderate positive correlation with the frequency of impulsive consumption (r = 0.46, p&amp;lt;.001). Further analysis reveals that emotion-inducing marketing and time-limited discount strategies exhibit the highest correlation with irrational consumption behaviors (r values of 0.39 and 0.41, respectively). Multiple linear regression analysis results demonstrate that, after controlling for confounding factors such as age, gender, and internet usage duration, online marketing sensitivity (β = 0.38, p&amp;lt;.001) and impulsive behavior levels (β = 0.41, p&amp;lt;.001) remain significant predictors of unplanned consumption behavior. Discussion The study reveals that individuals with mental disorders are more susceptible to emotional marketing messages and instant reward mechanisms in online marketing environments, which increases their risk of impulsive and irrational consumption behaviors. Their decision-making processes may be influenced by multiple factors, including emotional fluctuations, diminished cognitive control, and inadequate delayed gratification capacity. In the future, when formulating regulatory policies for online marketing and social support programs for patients with mental disorders, the consumption vulnerability of this group should be fully considered. By strengthening consumption risk warnings, enhancing digital literacy education and introducing psychological intervention mechanisms, their potential risks in the online consumption environment can be reduced. Funding No. 24A0741.

High arousal states, like fear and anxiety, play a crucial role in organisms’ adaptive responses to threats. Yet, inducing and reliably measuring such states within controlled settings presents challenges. This study uses a novel approach of CO 2 enriched air vs normal air in a Virtual Reality (VR) context to induce high arousal whilst measuring physiological signals such as galvanic skin response (GSR), facial skin impedance, facial electromyography (fEMG), photoplethysmography (PPG), breathing, and pupillometry. In a single-blind study, 63 participants underwent a regimen involving 20 min of breathing regular air followed by 20 min of 7.5% CO 2 , separated by a brief interval. Findings demonstrate the efficacy of CO 2 inhalation in eliciting high arousal, as substantiated by statistically significant changes for all signals, further validated through high (94%) accuracy arousal classification. This study establishes a method for inducing high arousal states within a laboratory context validated through comprehensive multi-sensor data and machine learning analyses. The study underscores the value of employing a suite of physiological measures to comprehensively describe the intricate dynamics of arousal. The generated database is a promising resource for researching physiological markers of arousal, panic, fear, and anxiety, offering insights that can potentially resonate within clinical and therapeutic realms. • Multi-modal, physiological database using CO 2 inhalation for emotional induction. • Rich array of physiological signals (EMG, PPG, IMU, GSR, pupillometry, respiration). • Effects of CO 2 inhalation on each collected measure are shown and analysed. • ML classifiers achieved high (94%) accuracy in distinguishing arousal conditions.